@inproceedings{hannayHowScientistsDevelop2009,
  title = {How Do Scientists Develop and Use Scientific Software?},
  booktitle = {Software {{Engineering}} for {{Computational Science}} and {{Engineering}}, 2009. {{SECSE}} '09. {{ICSE Workshop}} On},
  author = {Hannay, J. E. and Langtangen, H. P. and MacLeod, C. and Pfahl, D. and Singer, J. and Wilson, G.},
  date = {2009-05},
  pages = {1--8},
  publisher = {{IEEE / Dept. of Software Eng., Univ. of Oslo, Oslo}},
  doi = {10.1109/secse.2009.5069155},
  url = {https://doi.org/10.1109/secse.2009.5069155},
  abstract = {New knowledge in science and engineering relies increasingly on results produced by scientific software. Therefore, knowing how scientists develop and use software in their research is critical to assessing the necessity for improving current development practices and to making decisions about the future allocation of resources. To that end, this paper presents the results of a survey conducted online in October-December 2008 which received almost 2000 responses. Our main conclusions are that (1) the knowledge required to develop and use scientific software is primarily acquired from peers and through self-study, rather than from formal education and training; (2) the number of scientists using supercomputers is small compared to the number using desktop or intermediate computers; (3) most scientists rely primarily on software with a large user base; (4) while many scientists believe that software testing is important, a smaller number believe they have sufficient understanding about testing concepts; and (5) that there is a tendency for scientists to rank standard software engineering concepts higher if they work in large software development projects and teams, but that there is no uniform trend of association between rank of importance of software engineering concepts and project/team size.},
  isbn = {978-1-4244-3737-5},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-9217841,computational-science,data-uncertainty,modelling,modelling-uncertainty,software-engineering,software-errors,software-uncertainty,uncertainty},
  series = {{{SECSE}} '09},
  venue = {Washington, DC, USA}
}

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ICSE Workshop On","author":[{"propositions":[],"lastnames":["Hannay"],"firstnames":["J.","E."],"suffixes":[]},{"propositions":[],"lastnames":["Langtangen"],"firstnames":["H.","P."],"suffixes":[]},{"propositions":[],"lastnames":["MacLeod"],"firstnames":["C."],"suffixes":[]},{"propositions":[],"lastnames":["Pfahl"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Singer"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Wilson"],"firstnames":["G."],"suffixes":[]}],"date":"2009-05","pages":"1–8","publisher":"IEEE / Dept. of Software Eng., Univ. of Oslo, Oslo","doi":"10.1109/secse.2009.5069155","url":"https://doi.org/10.1109/secse.2009.5069155","abstract":"New knowledge in science and engineering relies increasingly on results produced by scientific software. Therefore, knowing how scientists develop and use software in their research is critical to assessing the necessity for improving current development practices and to making decisions about the future allocation of resources. To that end, this paper presents the results of a survey conducted online in October-December 2008 which received almost 2000 responses. Our main conclusions are that (1) the knowledge required to develop and use scientific software is primarily acquired from peers and through self-study, rather than from formal education and training; (2) the number of scientists using supercomputers is small compared to the number using desktop or intermediate computers; (3) most scientists rely primarily on software with a large user base; (4) while many scientists believe that software testing is important, a smaller number believe they have sufficient understanding about testing concepts; and (5) that there is a tendency for scientists to rank standard software engineering concepts higher if they work in large software development projects and teams, but that there is no uniform trend of association between rank of importance of software engineering concepts and project/team size.","isbn":"978-1-4244-3737-5","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-9217841,computational-science,data-uncertainty,modelling,modelling-uncertainty,software-engineering,software-errors,software-uncertainty,uncertainty","series":"SECSE '09","venue":"Washington, DC, USA","bibtex":"@inproceedings{hannayHowScientistsDevelop2009,\n title = {How Do Scientists Develop and Use Scientific Software?},\n booktitle = {Software {{Engineering}} for {{Computational Science}} and {{Engineering}}, 2009. {{SECSE}} '09. {{ICSE Workshop}} On},\n author = {Hannay, J. E. and Langtangen, H. P. and MacLeod, C. and Pfahl, D. and Singer, J. and Wilson, G.},\n date = {2009-05},\n pages = {1--8},\n publisher = {{IEEE / Dept. of Software Eng., Univ. of Oslo, Oslo}},\n doi = {10.1109/secse.2009.5069155},\n url = {https://doi.org/10.1109/secse.2009.5069155},\n abstract = {New knowledge in science and engineering relies increasingly on results produced by scientific software. Therefore, knowing how scientists develop and use software in their research is critical to assessing the necessity for improving current development practices and to making decisions about the future allocation of resources. To that end, this paper presents the results of a survey conducted online in October-December 2008 which received almost 2000 responses. Our main conclusions are that (1) the knowledge required to develop and use scientific software is primarily acquired from peers and through self-study, rather than from formal education and training; (2) the number of scientists using supercomputers is small compared to the number using desktop or intermediate computers; (3) most scientists rely primarily on software with a large user base; (4) while many scientists believe that software testing is important, a smaller number believe they have sufficient understanding about testing concepts; and (5) that there is a tendency for scientists to rank standard software engineering concepts higher if they work in large software development projects and teams, but that there is no uniform trend of association between rank of importance of software engineering concepts and project/team size.},\n isbn = {978-1-4244-3737-5},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-9217841,computational-science,data-uncertainty,modelling,modelling-uncertainty,software-engineering,software-errors,software-uncertainty,uncertainty},\n series = {{{SECSE}} '09},\n venue = {Washington, DC, USA}\n}\n\n","author_short":["Hannay, J. 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